Joint Channel Estimation and Symbol Detection in MIMO-OFDM Systems: A Deep Learning Approach using Bi-LSTM

Abstract

Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system is a promising technology that provides high capacity and high data rate transmission in 5G and beyond. Presence of a large number of antennas in a MIMO-OFDM system increases the overhead on pilot symbols used for channel estimation. Our proposed work exploits the channel sparsity to design a pilot based compressed sensing method for channel estimation and integrates a Bi-LSTM approach for symbol detection for improved performance. Additionally, we optimize the pilot symbols to minimize the error value to be effectively within the total power constraint. We evaluate the performance of our proposed system using mean square error (MSE) with least minimum mean square estimate (LMMSE) as the benchmark. We demonstrate the design and evaluation of a scalable and efficient approach to joint channel estimation and symbol detection in a MIMO-OFDM system using a fewer number of pilot symbols.